Coverage is an important performance metric in sensor networks. The traditional disk coverage model uses a very simple geometric relation between a sensor and its surrounding space points to capture the sensor's sensing capability and quality, which are not enough for many practical applications. In this article, motivated from the application of precision agriculture, we propose a new confident information coverage model for field reconstruction, where the objective is to obtain reconstruction maps of some physical phenomena's attribute with a given reconstruction quality for the whole sensor field, including points been sampled and not sampled. The proposed model is downward compatible with the disk coverage model, while it can greatly reduce sensor density for area coverage. Simulation results show that for the same reconstruction quality, the required sensor density based on the proposed new model is much less than that based on the disk model in both the deterministic and random sensor deployment. In practice, the proposed model helps to determine the number of sensors to be deployed for a given farmland and their locations in the deterministic deployment. The proposed model can also help to guide network operations for energy efficient data collection with guaranteed reconstruction quality.
In order to improve the quality of peak characteristics extraction in chromatographic data, an automatic peak fitting method based on wavelet ridges and morphology (WMPF) was proposed. In handling overlapping peaks and weak peaks, WMPF gave solution to deal with the shortcomings of existing peak detection methods. The ridge line of the signal was obtained by wavelet transform of the original chromatogram signal, and the signal was divided into peak areas and non-peak areas. Then, we processed morphological operations with an adaptive window, and combined Gaussian fitting with wavelet ridge information. Finally, the peak characteristics of the signal were extracted. Through the analysis of high-density lipoprotein electrophoretogram data, it was confirmed that the proposed method was effective for baseline separation, noise elimination, peak detection and overlapping peak recognition.
Yu, T.;Cao, P.*;Ji, X. Y.;Xie, L. K.;Huang, X. R.;An, Q.;Bai, H. Y.;Bao, J.;Chen, Y. H.;Cheng, P. J.;Cui, Z. Q.;Fan, R. R.;Feng, C. Q.;Gu, M. H.;Han, Z. J.;He, G. Z.;He, Y. C.;He, Y. F.;Huang, H. X.;Huang, W. L.
IEEE Transactions on Nuclear Science,2019年66(7):1095-1099 ISSN：0018-9499
[An, Q.; Feng, C. Q.; Liu, S. B.; Yu, T.; Huang, X. R.; Yu, L.; Cao, P.] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China.;[An, Q.; Feng, C. Q.; Liu, S. B.; Yu, T.; Huang, X. R.; Yu, L.; Cao, P.] Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China.;[Xie, L. K.; Ji, X. Y.] Univ Sci & Technol China, Dept Engn & Appl Phys, Hefei 230026, Peoples R China.;[Zhang, L. Y.; Sun, Z. J.; Xie, L. K.; Fan, R. R.; Ji, X. Y.] State Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China.;[Cui, Z. Q.; Bai, H. Y.; Jiang, H. Y.; Zhang, G. H.] Peking Univ, Sch Phys, State Key Lab Nucl Phys & Technol, Inst Heavy Ion Phys, Beijing 100871, Peoples R China.
[Cao, P.] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China.
21st IEEE-Nuclear-and-Plasma-Sciences-Society (NPSS) Real Time (RT) Conference
JUN 09-15, 2018
[Yu, T.;Cao, P.;Huang, X. R.;An, Q.;Feng, C. Q.;Liu, S. B.;Yu, L.] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China.^[Yu, T.;Cao, P.;Huang, X. R.;An, Q.;Feng, C. Q.;Liu, S. B.;Yu, L.] Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China.^[Ji, X. Y.;Xie, L. K.] Univ Sci & Technol China, Dept Engn & Appl Phys, Hefei 230026, Peoples R China.^[Ji, X. Y.;Xie, L. K.;Fan, R. R.;Sun, Z. J.;Zhang, L. Y.] State Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China.^[Bai, H. Y.;Cui, Z. Q.;Jiang, H. Y.;Zhang, G. H.] Peking Univ, Sch Phys, State Key Lab Nucl Phys & Technol, Inst Heavy Ion Phys, Beijing 100871, Peoples R China.^[Bao, J.;He, G. Z.;Huang, H. X.;Luan, G. Y.;Ren, J.;Ruan, X. C.;Tang, H. Q.;Wang, Q.;Wang, Z. H.;Wu, X. G.;Zhang, Q. W.;Zhong, Q. P.;Zhou, Z. Y.] China Inst Atom Energy, Beijing 102413, Peoples R China.^[Chen, Y. H.;Fan, R. R.;Gu, M. H.;He, Y. C.;Huang, W. L.;Ji, X. L.;Jiang, W.;Jing, H. T.;Kang, L.;Li, B.;Li, L.;Li, Q.;Li, X.;Ma, Y. L.;Ning, C. J.;Sun, H.;Sun, X. Y.;Sun, Z. J.;Tan, Z. X.;Tang, J. Y.;Wang, P. C.;Wang, Y. F.;Wang, Z.;Wu, Q. B.;Wu, X.;Yi, H.;Yu, Y. J.;Zhang, L. Y.;Zhang, J.;Zhou, L.;Zhu, K. J.] Chinese Acad Sci, Inst High Energy Phys, Beijing 100049, Peoples R China.^[Chen, Y. H.;Fan, R. R.;Gu, M. H.;He, Y. C.;Huang, W. L.;Ji, X. L.;Jiang, W.;Jing, H. T.;Kang, L.;Li, B.;Li, L.;Li, Q.;Li, X.;Ma, Y. L.;Ning, C. J.;Sun, H.;Sun, X. Y.;Sun, Z. J.;Tan, Z. X.;Tang, J. Y.;Wang, P. C.;Wang, Y. F.;Wang, Z.;Wu, Q. B.;Wu, X.;Yi, H.;Yu, Y. J.;Zhang, L. Y.;Zhang, J.;Zhou, L.;Zhu, K. J.] Dongguan Neutron Sci Ctr, Dongguan 523803, Peoples R China.^[Cheng, P. J.] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.^[Han, Z. J.;Liu, R.;Liu, X. Y.;Wen, J.;Wen, Z. W.;Yang, Y. W.] CAEP, Inst Nucl Phys & Chem, Mianyang 621900, Sichuan, Peoples R China.^[He, Y. F.] Univ South China, Sch Elect Engn, Hengyang 421001, Peoples R China.^[Song, Z. H.;Zhang, X. P.] Northwest Inst Nucl Technol, Xian 710000, Shaanxi, Peoples R China.^[Wang, T. F.] Beihang Univ, Sch Phys & Nucl Energy Engn, Beijing 100083, Peoples R China.^[Zhang, Q. M.;Zhao, Y. T.] Xi An Jiao Tong Univ, Dept Nucl Sci & Technol, Sch Energy & Power Engn, Xian 710049, Peoples R China.
Image fusion techniques aim at transferring useful information from the input source images to the fused image. The common assumption for most fusion approaches is that the useful information is defined by local features such as contrast, variance, and gradient. However, there is no consideration of global visual attention of the whole source images which indicates the "interesting" information of the source images. In this paper, we firstly review the patch-based image fusion methods which attract the attention and interest of many researchers. Then, a visual attention guided patch-based image fusion method is proposed. The visual attention maps of the source images are calculated from the sparse represent coefficients of the source images. Then, the sparse coefficients are fused with the guidance of visual attention maps in order to emphasize the global "interesting" objects in the source images. Finally, the fused image is reconstructed from the fused sparse coefficients. The new fusion strategy ensures that the objects being "interesting" for our visual system are preserved in the fused image. The proposed approach is tested on infrared and visual, medical, and multi-focus images. The results compared with those of traditional methods show obvious improvement in objective and subjective quality measurements. (C) 2014 Elsevier GmbH. All rights reserved.
Sensor scheduling;wireless sensor networks;multi-modal confident information coverage;set cover
Network lifetime maximization with guaranteed coverage is an important issue in wireless sensor networks. Based on our recently proposed confident information coverage (CIC) model, this paper studies the multi-modal confident information coverage (M2CIC) problem. Assuming that each node is equipped with different types of sensors, the objective is to schedule the multi-modal sensors' activity, such that the confident information coverage for each sensing modality can be guaranteed while the network lifetime can be maximized. We model the M2CIC problem as a multi-modal set cover problem (M2SC) and prove its NP-completeness. For solving the M2SC problem, we design two energy-efficient heuristics including a centralized one and a distributed one. In the proposed algorithms, different modal sensors are organized into a family of set covers, each of which can provide confident information coverage for all the monitored physical phenomena. Simulation results show that both the proposed algorithms can efficiently prolong the network lifetime and outperform two classical peer algorithms in terms of the extended network lifetime.
Based on the extended Huygens-Fresnel principle, we have derived the analytical expression of the average intensity of optical coherence lattices (OCLs) in oceanic turbulence with anisotropy, and then the beam quality parameters including the Strehl ratio (S-R) and the power-in-the-bucket (PIB) are obtained. One can find that the OCLs will eventually evolve into Gaussian shape with the periodicity reciprocity gradually breaking down when propagating through the anisotropic ocean water, and that the trend of evolving into Gaussian can be accelerated for increasing the ratio of temperature and salinity contributions to the refractive index spectrum omega, the lattice constant a and the rate of dissipation of mean square temperature chi(T) or decreasing the anisotropic factor xi and the rate of dissipation of turbulent kinetic energy per unit mass of fluid epsilon. Further, the S-R and PIB in the target plane under the effects of oceanic parameters are discussed in detail, and the S-R and PIB can be increased for the larger xi and epsilon or the smaller chi(T) and omega, namely, the beam quality becomes better. Our results can find potential application in the future optical communication system in an oceanic environment. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
In this study a novel Migrant Particle Swarm Optimization (Migrant PSO) algorithm is presented to upgrade the performance in multi-constraint trajectory optimization. To imitate the behaviour of a flock of migrant birds, the Migrant PSO algorithm integrates stochastic search and adaptive linear search mechanism effectively within both continuous space and discrete space. Then the developed algorithm is applied to the minimum control energy reentry trajectory optimization for X-33 vehicle model with free terminal time, some key issues such as parameterized method are discussed in detail. The effectiveness and efficiency of the proposed method are demonstrated by the comparison between the simulation result of the Migrant PSO algorithm and that of the Sequential quadratic programming (SQP) method.
Heating of ions by two Alfven waves propagating along an external magnetic field via nonresonant wave-particle interaction in low-beta plasmas is studied using test-particle simulation. Due to subcyclotron ion resonance, the heating effect of the left-hand polarized Alfven wave pair is 10% greater than that of the right-hand polarized pair. The results show that the perpendicular and parallel (to the external magnetic field) temperatures, as well as the parallel fluid velocity, vary sinusoidally with the phase difference. Furthermore, the magnitude of the oscillations decreases with the ratio of the frequencies of the two waves. When the frequency ratio reaches above 2, the effect of the phase difference vanishes. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4737896]
The aim of multi-focus image fusion is to create a synthetic all-in-focus image from several images each of which is obtained with different focus settings. However, if the resolution of source images is low, the fused images with traditional fusion method would be also in low-quality, which hinders further image analysis even the fused image is all-in-focus. This paper presents a novel joint multi-focus image fusion and super-resolution method via convolutional neural network (CNN). The first level network features of different source images are fused with the guidance of the local clarity calculated from the source images. The final high-resolution fused image is obtained with the reconstruction network filters which act like averaging filters. The experimental results demonstrate that the proposed approach can generate the fused images with better visual quality and acceptable computation efficiency as compared to other state-of-the-art works.
Barrier coverage<&wdkj&>barrier gap<&wdkj&>directional sensor networks<&wdkj&>Internet of Things (IoT)<&wdkj&>line-based deployment<&wdkj&>
The barrier coverage of a wireless sensor network is an important surveillance application of Internet of Things. Barrier coverage guarantees that all intruders traversing the protected region are detected by a chain of connected sensors. However, when the sensors are randomly deployed, barrier gaps may occur due to deployment randomness or insufficient sensors. How to locate the barrier gaps and mend them is an important aspect in the network. In this paper, we study the barrier gap problem in weak barrier coverage and strong barrier coverage that consist of directional sensors, and the sensors are deployed by a line-based deployment strategy. A gap-finding algorithm is proposed to find subbarriers and barrier gaps. Two gap-mending algorithms are devised to mend barrier gaps in the network: One algorithm is a simple rotation algorithm that only rotates two critical sensors in two subbarriers to fix the gap, and the other algorithm is a chain-reaction rotation algorithm that rotates sensors in the subbarrier in a chain-reaction manner to mend the gap. We conduct extensive simulations to evaluate the performance of the proposed algorithms. Simulation results show that the proposed gap-mending algorithms can effectively fix barrier gaps and improve the probability of barrier success construction.
Convolutional neural network;lung nodule candidate classification;multi-resolution model;knowledge transfer
The automatic lung nodule detection system can facilitate the early screening of lung cancer and timely medical interventions. However, there still exist multiple nodule candidates produced by initial rough detection in this system, and how to determine authenticity is a key problem. As this work is often challenged by the radiological heterogeneity of the computed tomography scans and the variable sizes of lung nodules, we put forward a multi-resolution convolutional neural network (CNN) to extract features of various levels and resolutions from different depth layers in the network for classification of lung nodule candidates. Through the use of knowledge transfer, the method can be divided into three steps. First, we transfer knowledge from the source CNN model which has been applied to edge detection and improve the model to a new multi-resolution model which is suitable for the image classification task. Then, the knowledge is transformed from source training progress so that all of the side-output branches in the model will be considered in the calculation. Moreover, the loss function and objective equation are improved to be image-wise calculation rather than pixel-wise. Finally, samples production and data enhancement are performed to train and test a classifier tailored for classification of lung nodule candidates. The experimental results on the LUNA16 data set show that our method gets an accuracy of 0.9733, a precision of 0.9673, and an AUC of 0.9954 while being used for lung nodule candidate classification, which is higher than the scores obtained by most of the state-of-the-art approach. In addition, when the test samples with three different sizes of 26* 26, 36* 36, and 48* 48 are used to test the multi-resolution CNN, the accuracy rate of all three experiments exceed 92.81%, which demonstrates that the proposed model is insensitive to input scales.
Measuring rail profile in the presence of multiple degrees of freedom vibration is a very challenging task. This paper presents a novel method based on the local affine invariant feature descriptor to calibrate distorted profiles, which are obtained by traditional rail measurement system. It has three major modules: local affine invariant (LAI) feature descriptor, affine transformation estimation and parameters refinement. LAI feature descriptor is based on the affine geometry invariant and generated by calculating the proportions of different areas. Using the proposed LAI descriptor, we implement a three-stage profile calibration including matching, estimation, and refinement based on grouping and fast iterative closest point (FICP) algorithm. The performance of proposed LAI descriptor and calibrating method is tested by performing extensive experiments. The experimental results show that our LAI descriptor is highly descriptive and robust with respect to varying resolution and noise, and the LAI descriptor based calibration is effective and repeatable. (C) 2017 Elsevier Ltd. All rights reserved.
[Liu, Jun] Shenzhen Univ, Int Collaborat Lab Mat Optoelect Sci & Technol 2D, Key Lab Optoelect Devices & Syst, Minist Educ & Guangdong Prov,Coll Optoelect Engn, Shenzhen 518060, Peoples R China.;[Liu, Jun] Aston Univ, Sch Engn & Appl Sci, Aston Inst Photon Technol, Birmingham B4 7ET, W Midlands, England.
We show that the group-velocity-led optical event horizon (OEH) in optical fibers provides a convenient way to actively control the propagation property of higher-order solitons by a comparatively weak dispersive wave (DW) pulse. It has been found numerically that clean soliton breakup, a process by which a second-order soliton completely splits into a pair of constituent solitons with vastly different power proportions after interacting with the weak DW pulse, will occur while external DWs become polychromatic. The temporal separation between both constituent solitons can be controlled by adjusting the power of the external DW. The more energetic main soliton is advanced/trailed in time depending on the selected frequency of input DW pulse. We have developed an analytic formalism describing the external acting-force (AF) perturbation. These results provide a fundamental explanation and physical scaling of optical pulse evolution in optical fibers and can find applications in improved supercontinuum sources. (c) 2017 Optical Society of America under the terms of the OSA Open Access Publishing Agreement